Introducing NFL Expected Points (12/20/16)

Today, we're introducing a new metric, Expected Points, for NFL games. Using simple box score statistics, we've developed an algorithm using regression analysis to predict the final score and win percentages for each team in any given game.

We intend to publish more on the statistic going forward as the building of the model is still fluid; however, we wanted to publish initial results before the end of the season. As one would assume, turnovers are extremely important to the outcome of NFL games. For that reason, we will be removing end of half turnovers (i.e. interceptions on a Hail Mary or fumble on lateral plays at the end of halves) since desperation-type plays do not provide any value to the outcome of the game.

A few things we learned in the creation of this Expected Points model are:
- Turnovers are even more important than we had previously perceived.
- Penalty yards matter, but they only have worthwhile, measurable effects when there are large disparities within a game.
- The location of turnovers are mostly random, but largely change the scoring and outcome of the game.

Here is a quick look each team's predicted score and win percentage for Week 15 in the NFL:

One immediate standout in the table above is the Bengals, who only had an expected score of 6.7 points and a 4.2% chance to win in a game that they led throughout. The results are largely in part due to the Steelers kicking six field goals and the Bengals gaining only 38 yards in the second half, after scoring on their first four drives to start the game (one of which was only 22 yards). Also, about 39% of their yardage gained was "lost" due to penalty yardage.

Another standout is the Jaguars-Texans game. The Texans would have been expected to win by a final score of approximately 18-to-10. In actuality, they need a last minute drive by back-up quarterback Tom Savage to win the game. This expectation mostly came from the result of the Jaguars gaining only 150 yards of total offense, committing 91 yards worth of penalties, and being gifted a few scores by Brock Osweiler.

The Packers stood a 79.7% chance to win based on our model; however, they needed a bomb to Jordy Nelson to pick up enough yardage for the game-winning field goal in Chicago. Our model expected a larger margin of victory mostly due to the Bears committing four turnovers (three of which were accounted for). The only reason the Bears were able to mount a large comeback was due to Davante Adams dropping two easy touchdowns.

As stated above, this is still a fluid method and we expect to make constant improvements going forward. A few potential avenues where we will look to improve the model are:
- Implementing special teams,
- Including starting field positions after kicks and/or turnovers,
- Score-adjusted yardage,
- Testing predictability which may include regressing turnover rates, especially for quarterback interception rates.

Needless to say, we are excited about the model's implemenations going forwards.